Table of contents

Data

The group variable selected was Sample_Group, and the donor variable was Sample_Name .

Finally, the selected samples added to the RGSet file were:

The following samples were excluded for the analysis:


Intensities boxplots

Green intensities

Red intensities


Failed probes

The probe failure rate is calculated for each sample and two thresholds are represented (5%, 10%).


Normalization options

The selected normalization method was Illumina. In the next plots, we can see the comparison of the processed data with the raw data.

After Minfi normalization, we obtained a GenomicRatioSet with some transformations depending on the type of normalization chosen. To understand better the different classes of Minfi packages, and their relations depending of the normalization method, please read this vignette of Minfi creators (it is also valid for Illumina EPIC arrays).

Options selected:

  • Drop SNPs: TRUE minimum MAF filtered: 0
  • Drop CpHs: TRUE
  • Drop X/Y Chromosomes: FALSE

After normalization, 56049 DNA methylation positions remained.


Density plot

Raw

Processed Illumina


SNPs Heatmap

The methylation array has 65 specific SNP probes. These SNP probes are intended to be used for sample tracking and sample mixups. Each SNP probe ought to have values clustered around 3 distinct values corresponding to homo-, and hetero-zygotes. Therefore, different samples of the same donor should cluster together.

Processed Illumina


Sex Prediction

Depending on the average chromosome Y signal and the average chromosome X signal is possible to predict the sex of the sample donors.

name sex
1 M
2 M
3 M
4 M
5 M
6 M
7 M
8 M
9 M
10 M
11 M
12 M
13 M
14 M
15 M
16 M


Batch effects

Correlating principal components with variables we can determine if Beta values are related to our variable of interest or other variables. This can also be useful to determine possible errors in the sample hybridization randomization and to select covariables to add to the linear model.

Not useful variables are discarded and the variable type is autodetected. The autodetected variable types were:


System and analysis information

This report was generated at 2021-02-16 17:09:35.

The session information was the following:

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=es_ES.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=es_ES.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=es_ES.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0 IlluminaHumanMethylationEPICmanifest_0.3.0         
 [3] minfi_1.36.0                                        bumphunter_1.32.0                                  
 [5] locfit_1.5-9.4                                      iterators_1.0.13                                   
 [7] foreach_1.5.1                                       Biostrings_2.58.0                                  
 [9] XVector_0.30.0                                      SummarizedExperiment_1.20.0                        
[11] Biobase_2.50.0                                      MatrixGenerics_1.2.1                               
[13] matrixStats_0.58.0                                  GenomicRanges_1.42.0                               
[15] GenomeInfoDb_1.26.2                                 IRanges_2.24.1                                     
[17] S4Vectors_0.28.1                                    BiocGenerics_0.36.0                                
[19] ggplot2_3.3.3                                       dplyr_1.0.4                                        
[21] shinycssloaders_1.0.0                               shinyWidgets_0.5.7                                 
[23] shinydashboardPlus_0.7.5                            shinydashboard_0.7.1                               
[25] shiny_1.6.0                                        

loaded via a namespace (and not attached):
  [1] BiocFileCache_1.14.0      plyr_1.8.6                lazyeval_0.2.2            splines_4.0.3             crosstalk_1.1.1          
  [6] BiocParallel_1.24.1       digest_0.6.27             htmltools_0.5.1.1         rsconnect_0.8.16          viridis_0.5.1            
 [11] magrittr_2.0.1            memoise_2.0.0             limma_3.46.0              readr_1.4.0               annotate_1.68.0          
 [16] askpass_1.1               siggenes_1.64.0           prettyunits_1.1.1         colorspace_2.0-0          blob_1.2.1               
 [21] rappdirs_0.3.3            xfun_0.21                 crayon_1.4.1              RCurl_1.98-1.2            jsonlite_1.7.2           
 [26] genefilter_1.72.1         GEOquery_2.58.0           survival_3.2-7            glue_1.4.2                registry_0.5-1           
 [31] gtable_0.3.0              zlibbioc_1.36.0           webshot_0.5.2             DelayedArray_0.16.1       Rhdf5lib_1.12.1          
 [36] HDF5Array_1.18.1          scales_1.1.1              DBI_1.1.1                 rngtools_1.5              Rcpp_1.0.6               
 [41] viridisLite_0.3.0         xtable_1.8-4              progress_1.2.2            bit_4.0.4                 mclust_5.4.7             
 [46] preprocessCore_1.52.1     DT_0.17                   htmlwidgets_1.5.3         httr_1.4.2                sourcetools_0.1.7        
 [51] gplots_3.1.1              RColorBrewer_1.1-2        ellipsis_0.3.1            farver_2.0.3              pkgconfig_2.0.3          
 [56] reshape_0.8.8             XML_3.99-0.5              sass_0.3.1                dbplyr_2.1.0              labeling_0.4.2           
 [61] later_1.1.0.1             tidyselect_1.1.0          rlang_0.4.10              reshape2_1.4.4            AnnotationDbi_1.52.0     
 [66] munsell_0.5.0             tools_4.0.3               cachem_1.0.3              generics_0.1.0            RSQLite_2.2.3            
 [71] evaluate_0.14             stringr_1.4.0             fastmap_1.1.0             yaml_2.2.1                heatmaply_1.2.1          
 [76] knitr_1.31                bit64_4.0.5               zip_2.1.1                 beanplot_1.2              caTools_1.18.1           
 [81] scrime_1.3.5              purrr_0.3.4               dendextend_1.14.0         packrat_0.5.0             nlme_3.1-151             
 [86] doRNG_1.8.2               sparseMatrixStats_1.2.1   mime_0.9                  nor1mix_1.3-0             xml2_1.3.2               
 [91] biomaRt_2.46.3            compiler_4.0.3            rstudioapi_0.13           plotly_4.9.3              curl_4.3                 
 [96] tibble_3.0.6              bslib_0.2.4               stringi_1.5.3             highr_0.8                 GenomicFeatures_1.42.1   
[101] lattice_0.20-41           Matrix_1.3-2              shinyjs_2.0.0             multtest_2.46.0           vctrs_0.3.6              
[106] pillar_1.4.7              lifecycle_0.2.0           rhdf5filters_1.2.0        jquerylib_0.1.3           data.table_1.13.6        
[111] bitops_1.0-6              seriation_1.2-9           httpuv_1.5.5              rtracklayer_1.50.0        R6_2.5.0                 
[116] promises_1.1.1            TSP_1.1-10                KernSmooth_2.23-18        gridExtra_2.3             codetools_0.2-18         
[121] MASS_7.3-53               gtools_3.8.2              assertthat_0.2.1          rhdf5_2.34.0              openssl_1.4.3            
[126] withr_2.4.1               GenomicAlignments_1.26.0  Rsamtools_2.6.0           GenomeInfoDbData_1.2.4    hms_1.0.0                
[131] quadprog_1.5-8            grid_4.0.3                tidyr_1.1.2               base64_2.0                rmarkdown_2.6            
[136] DelayedMatrixStats_1.12.3 illuminaio_0.32.0